The Classification of Profiles of Financial Catastrophe Caused by Out-of-Pocket Payments: A Methodological Approach

Maria-Carmen García-Centeno, Román Mínguez-Salido and Raúl del Pozo-Rubio
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Maria-Carmen García-Centeno: Department of Applied Mathematics and Statistics, CEU San Pablo University, Julian Romea 23, 28003 Madrid, Spain
Román Mínguez-Salido: Department of Public Economy, Statistics and Economic Policy, University of Castilla-La Mancha, Avenida Los Alfares 44, 16071 Cuenca, Spain
Raúl del Pozo-Rubio: Department of Economics and Finance, University of Castilla-La Mancha, Avenida Los Alfares 44, 16071 Cuenca, Spain

Mathematics, 2021, vol. 9, issue 11, 1-20

Abstract: The financial catastrophe resulting from the out-of-pocket payments necessary to access and use healthcare systems has been widely studied in the literature. The aim of this work is to predict the impact of the financial catastrophe a household will face as a result of out-of-pocket payments in long-term care in Spain. These predictions were made using machine learning techniques such as LASSO (Least Absolute Shrinkage and Selection Operator) penalized regression and elastic-net, as well as algorithms like k-nearest neighbors (KNN), MARS (Multivariate Adaptive Regression Splines), random forest, boosted trees and SVM (Support Vector Machine). The results reveal that all the classification methods performed well, with the complex models performing better than the simpler ones and showing no evidence of overfitting. Detecting and defining the profiles of individuals and families most likely to suffer from financial catastrophe is crucial in enabling the design of financial policies aimed at protecting vulnerable groups.

Keywords: financial catastrophe; out-of-pocket payment; machine learning; classification; profiles (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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